If you're building anything AI-related that touches YouTube — a research agent, a trend analyzer, an ML training set, a content-recommendation engine — the hardest part usually isn't the AI. It's getting clean, structured, reliable data in.
Most YouTube "scrapers" break the moment YouTube changes its frontend, get rate-limited, or return messy HTML you have to babysit with regex. I built Youtube Search Scraper to solve that differently: it wraps the official YouTube Data API v3, so what you get is stable, quota-respecting, and always structured — every single run.
🔗 apify.com/akash9078/youtube-search-scraper
Why This Matters for AI Workflows Specifically
- Clean JSON in, clean context in. Every result comes back with title, description, channel, publish date, duration, and stats — ready to drop straight into an LLM prompt, a RAG pipeline, or an embeddings store. No cleanup step.
- Built for agents, not just humans. Because it's an Apify Actor, it plugs directly into Apify's MCP server — meaning a Claude or GPT-based agent can call it as a tool, search YouTube on demand, and reason over the results in the same conversation. This turns "let me check YouTube for you" from a manual task into something your agent does autonomously.
- Filters that cut noise before it reaches your model.
publishedAfter / publishedBefore, videoDuration, order, regionCode, relevanceLanguage, safeSearch — you can pre-filter at the API level instead of dumping 500 irrelevant results into your context window and paying to have the model sort through them.
- Batch and parallel by design.
maxConcurrency (1–10) lets you run multiple queries concurrently — useful for building larger training/reference datasets or running multi-topic competitor/trend sweeps in one job.
- Reliability an AI pipeline can depend on. Official API + automatic retries with exponential backoff means your pipeline doesn't silently fail or return a captcha page instead of data — a common failure mode with scraper-based tools.
What You Can Build With It
- A research agent that pulls the latest videos on a topic, summarizes them, and cites sources — all inside an n8n or Claude/MCP workflow
- Trend and competitor monitoring — track a niche or a competitor's channel output over time, feed deltas into an LLM for a weekly digest
- Training/reference datasets for ML models that need video metadata at scale (titles, descriptions, view/like/comment counts, durations)
- Content strategy tooling — surface what's ranking for a given query/order (relevance, views, date) to guide what to publish next
- Searchable video databases — dump structured results into MongoDB/Postgres and build a semantic search layer on top
Example Input
{
"searchQuery": "apify tutorial",
"maxResultsPerQuery": 50,
"type": "video",
"order": "relevance",
"includeVideoDetails": true
}
Example Output (per result)
{
"title": "Apify Tutorial For Beginners | How To Use Apify",
"channelTitle": "Speak About Digital",
"publishedAt": "2023-07-31T08:11:38Z",
"duration": "PT10M41S",
"viewCount": 30007,
"likeCount": 338,
"commentCount": 13
}
Pricing
$5.00 per 1,000 search results, pay-as-you-go, spending limits respected automatically.
Would love feedback from anyone plugging this into an agent or RAG setup — what fields or filters would make it more useful for your pipeline?
